It is vital and essential to accurately and timely detect various damages of aircraft engines in civil aviation. Currently, aircraft engines are manually inspected via borescope images by aircraft maintenance technicians. This process is time-consuming and prone to error due to human factors. The aim of this paper is to automate the aircraft engine inspection, and this work presents a deep learning

With technological advances in the 21st century and the rise of the commercial space industry, crewed Mars missions to land humans on our red neighbor have become increasingly feasible. One of the key challenges in accomplishing this goal is determining whether model-based systems engineering training is necessary for space systems engineers to adopt digital engineering. This paper reviews model-based

Aircraft taxiing in large airports is often delayed by traffic conflicts. The increased fuel consumption and pollutant emission lower the efficiency of airport surface operation and bring potential safety hazards. Previous studies on route planning for surface taxiing seldom involve refined delay analysis under different traffic conflict types and discussions on route planning at airports with various

Although deep learning techniques have been successfully implemented to solve domain-specific unmanned aerial vehicle planning problems, it is still a challenging task to develop a learning method to solve multidomain planning problems. Because the multidomain problems often involve learning more parameters, a dilated dataset diminishes learning speed due to its size and high dimensionality. The following

Aerospace mission autonomy can improve mission capabilities, reduce the burden on ground operators, and prevent mission failure when encountering unexpected situations. However, autonomy comes at the cost of additional complexity for design, development, and testing. This research presents an autonomous mission framework to bridge the gap between existing aerospace mission systems and emerging autonomous

Low-altitude urban flight planning for small unmanned aircraft systems (UASs) accurate vehicle kinodynamics, environment maps, and risk models to assure that flight plans consider the urban landscape as well as airspace constraints. This paper presents a suite of motion planning metrics designed for small UAS urban flight and defines map-based and path-based metrics to holistically characterize motion

Micro-aerial vehicles (MAVs) lack the endurance times demanded by typical mission applications, and previous work to minimize flight path disturbances has also quantified their high atmospheric sensitivities. This study introduces an approach to modulate the sensitivity through real-time parameter variation that translates into a net energy gain of the vehicle. By applying a control-theoretic disturbance

The goal of this work was to investigate the feasibility of developing machine learning models for predicting the values of aircraft configuration design variables when provided with time series of mission-informed performance parameters. Regression artificial neural networks, along with their associated training data, have been generated and tested for aircraft design space exploration scenarios.

The recent widespread implementation of Automatic Dependent Surveillance–Broadcasting (ADS-B) systems on aircraft allows for improved monitoring and air traffic control management. As part of this monitoring, it is important to be able to detect unusual flight trajectories due to weather events, detection avoidance, aircraft malfunction, or other activities that may signal anomalous behavior. Given

Synthetic aperture radar (SAR) enables imaging of topographic surfaces day and night in different atmospheric conditions. SAR imaging systems record both intensity and phase information of the backscattered signals. Acquired intensity information is often exposed to speckle noise, and gathered phase information is usually corrupted by thermal and other types of noise. Thus, these types of noises have

This work explores on-board planning for the single spacecraft, multiple ground station Earth-observing satellite scheduling problem through artificial neural network function approximation of state–action value estimates generated by Monte Carlo tree search (MCTS). An extensive hyperparameter search is conducted for MCTS on the basis of performance, safety, and downlink opportunity utilization to

The use of finite-state methods is critical to the development of accurate and efficient inflow models used in rotorcraft flight dynamics simulation and control. Recent work in the finite-state field has allowed for the application of these models to multirotor systems using the adjoint theorem, which involves time delays and adjoint variables. However, the addition of time delays and adjoint variables

A novel deep multi-agent reinforcement learning framework is proposed to identify and resolve conflicts among a variable number of aircraft in a high-density, stochastic, and dynamic en route sector. The concept of using distributed vehicle autonomy to ensure separation is proposed, instead of a centralized sector air traffic controller. Our proposed framework uses proximal policy optimization that

The reconstruction of atmospheric properties encountered during Mars entry trajectories is a crucial element of postflight mission analysis. This paper proposes a deep learning architecture using a long short-term memory (LSTM) network for the reconstruction of Martian density and wind profiles from inertial measurements and guidance commands. The LSTM is trained on a large set of Mars entry trajectories

Current aircraft flight deck interfaces do not provide information on how a performance-altering failure constrains an aircraft’s flight envelope. As a result, it is difficult for flight crews to plan maneuvers in order to reach navigation targets. This study presents the results of the conceptual development of constraint-based interface symbology that aims to address this issue. The proposed symbology

In recent years, there has been a rapid growth in applying machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors

Within the framework of complex system analyses, such as aircraft and launch vehicles, the presence of computationally intensive models (e.g., finite element models and multidisciplinary analyses) coupled with the dependence on discrete and unordered technological design choices results in challenging modeling problems. In this paper, the use of Gaussian process surrogate modeling of mixed continuous/discrete

This paper presents a novel method for localization and recognition of moving objects in a real airport surface scene. Different from the traditional applications, moving object detection (MOD) in the airport surface is more challenging because the background is an open outdoor environment, which means that the target objects are usually low in resolution and the MOD task is vulnerable to many undesired

This paper investigates multi-objective optimization of coordinated patrolling flight of multiple unmanned aerial vehicles in the vicinity of terrain, while respecting their performance parameters. A new efficient modified A-star (A*) algorithm with a novel defined criterion known as individual revisit time cell value is introduced and extended to the whole area of the three-dimensional mountainous

Airlines traditionally gather weather information before departure to generate flight routes that avoid hazardous weather while minimizing flight time. However, flight crews may have to perform in-flight replanning as weather information can significantly change after departure. This in-flight replanning activity is currently not fully automated, which has the potential to increase crew workload and

Digital engineering is increasingly introduced for managing and supporting the development of systems for space. However, few academic teams have the competency needed to manage projects using digital engineering and systems engineering. The subject of this paper is an academic CubeSat project in which a variety of digital engineering techniques are used. The tailoring that has been applied to fit

Accurate detection and segmentation of obstacles is the key to the smooth operation of the planetary rovers and the basic guarantee of scientific exploration mission. The traditional method of rock segmentation based on boundary detector is affected by the change of illumination and dust storms. To address this problem, this paper proposes an improved U-net-based architecture combined with Visual Geometry

This paper aims to provide a resiliency management strategy of the air transportation network through network stability theory. To ensure the network to recover quickly from an upset condition, an optimization problem has been introduced through network stability upon the meta-population epidemic process model, which is a low-dimensional approximate model of the air traffic flow network. The delay

Journal of Aerospace Information Systems, Ahead of Print.

This paper describes the design and implementation of Black Lion, a purely software-based, distributed environment for integrated testing of independent spacecraft mission models. The Black Lion simulation environment is architected to be reconfigurable, allowing for any number of heterogenous software models, across one or multiple computing platforms, to be integrated into a single spacecraft simulation

In this study, reinforcement learning (RL)-based centralized path planning is performed for an unmanned combat aerial vehicle (UCAV) fleet in a human-made hostile environment. The proposed method provides a novel approach in which closing speed and approximate time-to-go terms are used in the reward function to obtain cooperative motion while ensuring no-fly-zones (NFZs) and time-of-arrival constraints

This paper proposes an autonomous navigation solution for an unmanned aerial vehicle (UAV) in an unknown and Global Navigation Satellite System (GNNS)-denied environment using a goal-aware robocentric reference frame. A combined Proportional Navigation (PN)-guidance and obstacle avoidance controller is developed to help navigate the UAV to a user-defined goal by successfully avoiding obstacles along

This paper aims to examine the potential of using the emerging deep reinforcement learning techniques in missile guidance applications. To this end, a Markovian decision process that enables the application of reinforcement learning theory to solve the guidance problem is formulated. A heuristic way is used to shape a proper reward function that has tradeoff between guidance accuracy, energy consumption

Journal of Aerospace Information Systems, Ahead of Print.

The use of deep neural networks (DNNs) in terrestrial applications went from niche to widespread in a few years, thanks to relatively inexpensive hardware for both training and inference, and large datasets available. The applicability of this paradigm to space systems, where both large datasets and inexpensive hardware are not readily available, is more difficult and thus still rare. This paper analyzes

Collision avoidance for fixed-wing unmanned aerial vehicles (UAVs), using a novel methodology of avoidance map, is addressed in this work. Avoidance map is defined as a mapping in the control input space of a pair of UAVs, which partitions the control space into avoidance and collision regions, respectively. The choice of maneuvers to execute successful avoidance can be effectively obtained by using

This paper presents a method for modeling and simulation of Global Navigation Satellite System (GNSS) constellations by the use of Chebyshev polynomials fit to publicly available precision data. The Global Positioning System (GPS) is used to demonstrate the method, but the method applies to all GNSS with precise ephemeris data including Galileo and GLONASS. The method facilitates highly accurate satellite

In high-density urban air mobility (UAM) operations, mitigating congestion and reducing structural constraints are key challenges. Pioneering urban airspace design projects expect the air vehicles to fit into structured UAM corridor networks. However, most existing air transport networks are not capable of handling the increasing traffic demand, which is likely to cause congestion, traffic complexity

This paper introduces an optimal scheduling framework for the Earth observation tasks of multiple agile satellites based on nonlinear modeling of attitude change time for the task transition. An attitude control module using a quaternion feedback constructs the database storing the time to complete the attitude change maneuver for a given initial and final attitude pair. A mixed-integer linear programming

This paper presents a practical method to compute the four-dimensional flight trajectories of aircraft in the presence of winds. The proposed method consisted of numerically integrating the aircraft equations of motion over different segments that compose a typical commercial flight profile. For this purpose, the aircraft vertical trajectory was divided into seven typical flight segments: unrestricted

Journal of Aerospace Information Systems, Ahead of Print.

Journal of Aerospace Information Systems, Ahead of Print.

To achieve system resilience, one can leverage high-level design features (e.g., redundancies and fail-safes), adjust operational profiles (e.g., load or trajectory), and use appropriate contingency management (e.g., emergency procedures) to mitigate potential hazards. For example, in the design of a novel drone, one would optimize the rotor and battery pack architectures (design), flight-plan (operations)

Journal of Aerospace Information Systems, Ahead of Print.

Journal of Aerospace Information Systems, Ahead of Print.

This work proposes a fast algorithm for generating obstacle-free and wind-efficient flight paths at a constant above-ground-level altitude in urban environments because a fast flight path planning algorithm is an essential function or service needed for enabling small unmanned aerial vehicle (sUAV) to operate in urban environments within Class G airspace. The proposed method first converts the 3D path

This work addresses the iterated nonstationary assistant selection problem, in which over the course of repeated interactions on a mission, an autonomous robot experiencing a fault must select a single human from among a group of assistants to restore it to operation. The assistants in our problem have a level of performance that changes as a function of their experience solving the problem. Our approach

Rosetta was a European Space Agency (ESA) cornerstone mission that launched in March 2004, exited hibernation in January 2014, entered orbit around the comet 67P/Churyumov-Gerasimenko in August 2014, and escorted the comet through September 2016, executing the most detailed study of a comet ever undertaken by humankind. The Rosetta Orbiter had 11 scientific instruments (4 remote sensing) and the Philae

Location privacy of aircraft has recently gained attention as air traffic management was modernized using novel surveillance technologies. Business aviation circles and various military and government entities voiced serious concerns about automated and ubiquitous tracking. Consequently, some flight authorities have started addressing these operational privacy issues with novel programs. A first analysis

Unmanned aerial systems (UASs) are continuing to proliferate. Quantitative risk assessment for UAS operations, both a priori and during the operation, are necessary for governing authorities and insurance companies to understand the risks and properly approve operations and assign insurance premiums, respectively. In this paper, the problem of UAS risk analysis and decision making is treated through

A policy for six-degree-of-freedom docking maneuvers with rotating targets is developed through reinforcement learning and implemented as a feedback control law. Potential clients for satellite servicing and orbital debris objects are often rotating around a constant axis within their respective Earth orbits. In the context of such missions, reinforcement learning provides an appealing framework for

The stabilized approach is important to avoid aircraft accidents during landing. Although there are many possible factors that can lead to an unstable approach, actions can be taken to mitigate the risk if the reason for unstable approaches is identified properly. However, because unstable approaches are rarely observed, it is difficult to identify the relevant reasons by analyzing unstable approaches

Two image-based sensing methods are merged to mimic human vision in support of airborne detect-and-avoid and counter–unmanned aircraft systems applications. In the proposed sensing system architecture, a peripheral vision camera (with a fisheye lens) provides a large field of view, whereas a central vision camera (with a perspective lens) provides high-resolution imagery of a specific target. Beyond

There is a growing interest among scientists to coordinate diverse and temporally responsive measurements from multiple space and ground-based observatories in order to obtain greater insights into transient scientific events than would otherwise be possible. Many transient scientific events of interest occur randomly and the scientific value of follow-up observations decays with time. As a result

Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation in autonomous small unmanned aerial system (sUAS) operations. Machine vision systems, based on standard optics and visible light detectors, possess the ideal size, weight, and power (SWaP) requirements for sUAS. This work presents the design and analysis of a novel aircraft detection and tracking pipeline based

An autonomous guidance and flight control system was integrated and flight tested on a Black Hawk helicopter as part of an effort to provide all weather capability for U.S. Army fleet rotorcraft. The guidance and flight-control system components were previously flight tested on a full-authority helicopter, and were adapted to fly on the partial-authority test helicopter of this paper. The main autonomy

This work proposes a set of complementary practices to agile methodologies, aiming at adapting them to the development of critical aerospace embedded systems by distributed teams. To identify the main gaps in this context, two approaches are used. The first one confronts the main activities required by aerospace standards for the development of critical embedded systems with a set of compiled agile

This paper presents a computer architecture with the ability to respond to radiation-induced faults. The architecture presented advances the state-of-the-art by implementing a redundant multiprocessor system on a commercial-off-the-shelf field programmable gate array. The system, called RadPC, can respond to radiation-induced faults and undergo repairs as necessary. A reliability analysis is conducted

Delay tolerant networks (DTNs) offer a set of standardized protocols to enable Internet-like connectivity across the solar system. Unlike other protocols such as the Transmission Control Protocol (TCP) and the Internet Protocol (IP), DTN protocols are robust to end-to-end connection disruptions and long delays. Although the behavior of DTN core protocols is well understood, management of DTNs is still

Active attitude control systems using a novel six-layer electromagnetic embedded printed air coil for small satellites with various dimensions are presented in this paper. The proposed designs are optimized in terms of available area, generated torque, power dissipation, and generated magnetic dipole moment. The designed printed air coils are the best choice for small satellites’ attitude stabilization

This paper describes three approaches to enabling a severely computationally limited embedded scheduler to consider a small number of alternative activities based on resource availability. This paper considers the case where the scheduler is so computationally limited that it cannot backtrack search. The first two approaches precompile resource checks (called guards) that only enable selection of a

Tracking space objects is important for managing space traffic and predicting collisions, but is difficult in part due to data association and orbit model uncertainty. Expectation–maximization (EM) is a commonly used tracking method that has not been widely considered for tracking space objects. The technique consists of iteratively computing data association probabilities with a set of current element

Increased interest in body–machine interfaces necessitates understanding how to train users to use nontraditional inputs. In this study, a control task driven by subject-activated surface electromyography was developed as a testbed to observe the effects of automated training methodologies on the development of performance, workload, and trust. Forty-eight subjects learned to use a surface-electromyography-based

Scheduling the coverage of a celestial body by scientific instruments onboard multiple spacecraft under observational constraints is central in a significant number of current and future missions for the exploration of the solar system. This paper describes the components and algorithms of a software system used to study early-phase mission design or to schedule daily operations of currently in-flight

Deep reinforcement learning was used to train an agent within the framework of a Markov decision process (MDP) to pursue a target, while avoiding a defender, for the target–attacker–defender (TAD) differential game of pursuit and evasion. The aim of this work was to explore the games where the previous attacking guidance methods found in literature failed to capture the target. The reward function